Long-tailed distribution of remote sensing data generally limits the object recognition performance of deep neural networks. We notice that too many samples from head class will induce the neural network to learn features of tail class samples being biased toward the head. To solve this, we propose a novel center-wise feature consistency learning (CFCL) mechanism for long-tailed remote sensing object recognition. First, we implement a head-tail center feature generation procedure that builds two teacher models to extract the knowledge from the head class and tail class samples, respectively, so as to avoid the extracted tail class features being affected by the head classes. Second, a CFCL strategy is introduced, which distills the central feature of each class to a student model, thereby making the classification boundaries more prominent. Especially, the central feature is estimated by referring to the features which are correctly classified by the teacher models, thus the inaccurate knowledge is abandoned. Extensive experiments on widely adopted remote sensing recognition datasets including FGSC-23, DIOR, xView, and HRSC2016 demonstrate that our method achieves superior performance compared to the state-of-the-art approaches. Code and data are available at: https://github.com/wdzhao123/CWFC .

中文翻译：

---

长尾遥感物体识别的中心特征一致性学习